Energy consumption is an important issue in continuous wireless telemonitoring of physiological signals. Compressed sensing (CS) is a promising framework to address it, due to its energy-efficient data compression procedure. However, most CS algorithms have difficulty in data recovery due to non-sparsity characteristic of many physiological signals. Block sparse Bayesian learning (BSBL) is an effective approach to recover such signals with satisfactory recovery quality. However, it is time-consuming in recovering multichannel signals, since its computational load almost linearly increases with the number of channels.
This work proposes a spatiotemporal sparse Bayesian learning algorithm to recover multichannel signals simultaneously. It not only exploits temporal correlation within each channel signal, but also exploits inter-channel correlation among different channel signals. Furthermore, its computational load is not significantly affected by the number of channels. The proposed algorithm was applied to brain computer interface (BCI) and EEG-based driver's drowsiness estimation. Results showed that the algorithm had both better recovery performance and much higher speed than BSBL. Particularly, the proposed algorithm ensured that the BCI classification and the drowsiness estimation had little degradation even when data were compressed by 80%, making it very suitable for continuous wireless telemonitoring of multichannel signals.

The implementation for ST-SBL is here. This is very interesting as we know Zhilin has been devloping one of the solvers that goes beyond sparsity for reconstruction.

...The goal of this competition is to predict the category of a visual stimulus presented to a subject from the concurrent brain activity. The brain activity is captured with an MEG device which records 306 timeseries at 1KHz of the magnetic field associated with the brain currents. The categories of the visual stimulus for this competition are two: face and scrambled face. A stimulus and the concurrent MEG recording is calledtrial and thousands of randomized trials were recorded from multiple subjects. The trials of some of the subjects, i.e. the train set, are provided to create prediction models. The remaining trials, i.e. the test set, belong to different subjects and they will be used to score the prediction models. Because of the variability across subjects in brain anatomy and in the patterns of brain activity, a certain degree of difference is expected between the data of different subjects and thus between the train set and the test set...

Why am I mentioning this ?

Because if one uses the right dictionary, ST-SBL might provide a good framework for choosing the right feature for consumption in the translational competition set up by Kaggle. How so ? Since you are paying attention, you know that random projections can be equivalent to PCA and that this randomly generated phi matrix is key to getting to 80 percent compression in Zhilin et al's paper. In other words, if one usesa random phi and ST-SBL to get similar results as that of the paper with the Kaggle dataset, that will mean the random projections (phi matrix) can provide good features that can then be classified.